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Record W2541549572 · doi:10.1097/acm.0000000000001370

The Use of the Delphi and Other Consensus Group Methods in Medical Education

2016· article· en· W2541549572 on OpenAlexaff
Susan Humphrey‐Murto, Lara Varpio, Timothy J. Wood, Carol Gonsalves, Lee‐Anne Ufholz, Thomas Foth

Bibliographic record

VenueAcademic Medicine · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsycINFODelphi methodMEDLINEDelphiScopusMedical educationData extractionStandardizationPsychologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Purpose: The Delphi and other consensus methods are a systematic means to measure and develop consensus when empirical evidence is lacking or contradictory. They aim to determine the extent to which experts agree about a particular issue, with the ultimate goal of providing a unified expert opinion. In medical education, there are several important areas of inquiry that are plagued by high levels of uncertainty and limited evidence-based literature. Consequently, consensus group methods are relevant to medical educators. Despite extensive use in other fields, consensus methods are poorly standardized and inconsistently described. Several articles highlight significant deficiencies in methodology and reporting.1,2 Given these deficiencies, the following four questions are addressed: (1) How extensively are consensus methods used in medical education research? (2) What types of methods are used? (3) For what purpose? (4) Is there standardization in the application and reporting of the methods? Method: MEDLINE, Embase, PsycINFO, PubMed, Scopus, and ERIC databases were searched for articles focusing on medical education and using the following keywords: “Delphi,” “RAND,” “nominal group,” and “consensus group methods” (2009–2013). Inclusion criteria included English-language and full-text articles of completed research. A standardized extraction form was developed to evaluate the methodology and quality of reporting. Through an iterative process the form and definitions were refined. The final data extraction form consisted of two parts: (1) a section to gather demographic information, such as type of consensus group used and purpose of the project; and (2) specific features reflecting methodological rigor, such as reporting of literature review, number of participants in each round, type of feedback provided, and definition of consensus. Results: The initial search yielded 692 articles. After removal of duplicates, 143 full-text articles met inclusion criteria. Based on previous reviews, this number was deemed to be a sufficiently representative sample. The consensus methods described were the Delphi (40.6%), modified Delphi (31.5%), nominal group technique (NGT) (11.2%), and various other combinations (e.g., Delphi and NGT) (16.7%). The most common purposes were for curricular development or renewal (25.9%), assessment tool development (21%), and defining competencies (10.5%). The quality of reporting was variable; 107/143 (66.4%) described that a literature review was conducted in preparation for the questionnaire, 36/143 (25.2%) described what background information was provided to participants, 93/143 (65%) provided the response rates, 59/143 (41.3%) reported if private decisions were collected, 50/143 (35%) described formal feedback of group ratings, and 48/143 (33.6%) defined consensus a priori. Conclusions: This study of consensus group methods used in the medical education literature highlights the considerable variability in reporting. Studies do not consistently provide sufficient detail about methods, thus leading to a lack of scientific credibility. If consensus methods should inform best education practice, they must be rigorously conducted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.298
GPT teacher head0.571
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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